Energy storage intelligent temperature control system

Abstract

The invention discloses an energy storage intelligent temperature control system, and relates to the technical field of intelligent temperature control. Dynamic optimization of the dynamic management and control strategy is completed; the low-energy-consumption temperature control equipment control strategy at the next moment is formulated, dynamic optimization and self-adaptive adjustment of the temperature control strategy of the energy storage system are achieved, the overall energy consumption and operation noise are remarkably reduced, the adaptive capacity of the temperature control system to complex and changeable working conditions is improved, unification of energy conservation, accuracy and intelligence is achieved, and the method is suitable for popularization and application. The technical problems of single control strategy, high energy consumption and poor environmental adaptability in the prior art are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent temperature control technology, and in particular to an intelligent temperature control system for energy storage. Background Technology

[0002] Currently, although there is a wealth of research on control strategies for temperature control equipment, with many studies starting from the design of the temperature control equipment system itself to modify it to obtain different control strategies and effects, or to implement zone control according to different areas of different scenarios, there is a severe lack of implementation plans based on the directly controllable parameters of the temperature control equipment system according to the real-time environmental conditions.

[0003] Currently, most implementation cases or solutions only control the outlet water temperature of the liquid chiller unit to achieve the purpose of temperature control in the energy storage system. Although this can meet the system temperature control requirements to a certain extent, it cannot achieve the optimal overall energy consumption and environmental protection of the surrounding environment. Furthermore, considering the appropriate charging and discharging temperature of the battery cells in the energy storage system, after determining the control parameters of the temperature control equipment, attention is paid to the impact of the outlet water temperature and water flow rate of the temperature control equipment on the target temperature of the battery cells in the system. However, no detailed control strategies are made for the complex real environment, and the relationship between operable parameters and influencing factors is rarely involved. Summary of the Invention

[0004] In view of the problems existing in the existing intelligent temperature control system for energy storage, this invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is: To address the aforementioned technical problems, this invention provides the following technical solution: How to overcome the problems of high energy consumption and poor adaptability caused by the rigid temperature control strategy of existing energy storage systems and the single regulation of outlet water temperature? By constructing an intelligent control model based on deep learning, the control strategy can be dynamically optimized in real time according to the changes in multiple environmental factors inside and outside the chamber, so as to simultaneously meet the cell temperature control requirements, reduce system energy consumption, and improve environmental adaptability.

[0006] In a first aspect, embodiments of the present invention provide an intelligent temperature control method for energy storage, which includes: constructing a temperature control and management model based on deep learning and formulating a dynamic control and management strategy; Set up a deep learning mechanism, select control actions, update the temperature control model based on feedback results, and complete the dynamic optimization of the dynamic control strategy; Based on the updated temperature control model according to the feedback results, the decision-making system formulates a low-energy-consumption temperature control strategy for the next moment based on the feedback of the operating status of the intelligent temperature control system for energy storage.

[0007] As a preferred embodiment of the intelligent temperature control method for energy storage described in this invention, the construction of the temperature control management model based on deep learning includes constructing a deep learning algorithm for intelligent management and control of temperature control equipment that considers multiple factors based on deep learning technology. The formulation of dynamic management and control strategies includes developing dynamic management and control strategies that respond to changes in the internal and external environment of the energy storage system to meet the temperature requirements and low energy consumption of the battery cells with high adaptability.

[0008] As a preferred embodiment of the intelligent temperature control method for energy storage described in this invention, the deep learning mechanism includes states, actions, and rewards. Based on the current environment and operating status Choose the relative action If the result of the action leads to the desired effect or outcome, it will be considered a reward. Otherwise, if the result of the action deviates from the target effect, it will be punished.

[0009] As a preferred embodiment of the intelligent temperature control method for energy storage described in this invention, the step of updating the temperature control management model based on feedback results includes using a weighted macro average as an evaluation index for the prediction effect of the classification algorithm in the prediction stage of the temperature control management model, which comprehensively reflects both reward and punishment indicators. The classification algorithm includes calculating the influence levels for the hidden layers. For each category i at each level, the binary classification result is calculated using the binary classification formula, denoted as... The calculation is performed based on the weight of each level in the hidden layer, and the precision of the reward and the recall of the penalty are calculated.

[0010] As a preferred embodiment of the intelligent temperature control method for energy storage described in this invention, the formula for calculating the allocation based on the weight of each level in the hidden layer is as follows:

[0011] in, This represents the total number of levels in the hidden layers. This represents the percentage of influencing parameters in the entire parameter library for the corresponding mode / scenario. For overall weighted macro average value, For the i-th hidden layer category in the binary classification case value, The penalty recall rate is the value corresponding to the i-th hidden layer category. Let be the reward precision corresponding to the i-th hidden layer category.

[0012] In a preferred embodiment of the intelligent temperature control method for energy storage described in this invention, the formula for calculating the reward accuracy is:

[0013] The formula for calculating the penalty recall rate is as follows:

[0014] in, To predict the number of items that conform to the trend, To predict the number of items that contradict the trend; This represents the total number of cumulative predictions. To reward accuracy, To penalize low recall rates.

[0015] As a preferred embodiment of the energy storage intelligent temperature control method of the present invention, the step of formulating a low-energy-consumption temperature control device control strategy for the next moment includes: Collect operating parameters of the energy storage system, including external dry bulb temperature, internal temperature, internal humidity, charge / discharge power, and cycle count. The collected parameters are input into the deep learning algorithm for predicting the comfort of the battery cells in the cabin, predicting the target temperature control requirement of the battery cells at the next moment, and determining whether the conditions for starting the energy-saving mode are met. If the conditions for starting the energy-saving mode are met, the liquid chiller unit will start the self-circulation and dehumidification air conditioning combination mode. The temperature control speed of the liquid chiller pump, the opening frequency of the dehumidification air conditioner and the set temperature will be dynamically adjusted according to the algorithm output. The control parameters for starting the energy-saving mode will be sent to the control energy storage system and executed. If the conditions for energy-saving mode are not met, the liquid-cooled unit will start the cooling mode. Depending on the intensity of cooling demand, the dry cooler will be selected for natural heat dissipation, coupled cooling of the dry cooler and compressor, or pure compressor cooling. The outlet water temperature and unit power will be adjusted. After the temperature control of the energy storage system reaches the set target, the corresponding control parameters will be output to the control energy storage system and executed.

[0016] Secondly, embodiments of the present invention provide an intelligent temperature control system for energy storage, comprising: a dynamic management and control strategy module, which constructs a temperature control and control model based on deep learning and formulates a dynamic management and control strategy; a dynamic optimization module for the dynamic management and control strategy, which sets up a deep learning mechanism, selects control actions, updates the temperature control and control model based on feedback results, and completes the dynamic optimization of the dynamic management and control strategy; and a temperature control equipment control strategy module, which, based on the temperature control and control model updated by feedback results, decides on the feedback of the operating status of the intelligent temperature control system for energy storage and formulates a low-energy-consumption temperature control equipment control strategy for the next moment.

[0017] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement any step of the above-described intelligent temperature control method for energy storage.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the above-described intelligent temperature control method for energy storage.

[0019] The beneficial effects of this invention are as follows: By constructing an intelligent temperature control model based on deep learning and deep reinforcement learning, this invention achieves dynamic optimization and adaptive adjustment of the temperature control strategy for energy storage systems. Compared with the traditional static control method that only fixes and adjusts the outlet water temperature of the liquid chiller, this invention comprehensively considers multi-dimensional environmental and operational factors such as internal and external temperature, humidity, charging and discharging power, and cycle count. It can sense changes in operating conditions in real time, predict cell temperature control requirements, and dynamically decide on the optimal operating mode and control parameters. By introducing a state-action-reward mechanism for model iterative optimization, combined with weighted macro averaging... The evaluation strategy of the index enables the system to meet the high temperature requirements of the battery cells while prioritizing the use of low-energy-consumption modes such as self-circulation, dehumidification, and dry cooler, which significantly reduces the overall energy consumption and operating noise. This invention improves the adaptability of the temperature control system to complex and variable operating conditions, and achieves a unity of energy saving, accuracy and intelligence. It effectively solves the technical problems of single control strategy, high energy consumption and poor environmental adaptability in the existing technology. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 A flowchart of an energy storage intelligent temperature control method provided in an embodiment of the present invention.

[0021] Figure 2 This is a system schematic diagram of the intelligent temperature control method for energy storage provided in an embodiment of the present invention.

[0022] Figure 3 A schematic diagram of reward and penalty indicators for the intelligent temperature control method for energy storage provided in this embodiment of the invention.

[0023] Figure 4 This is a schematic diagram illustrating the execution of the control strategy for the intelligent temperature control method for energy storage provided in an embodiment of the present invention.

[0024] Figure 5 The diagram illustrates the impact of the energy storage intelligent temperature control method provided in this embodiment of the invention on the dehumidifier and liquid cooling unit circulation.

[0025] Figure 6A schematic diagram of the deep learning control algorithm for the air-cooled heat dissipation management mode of the intelligent temperature control method for energy storage provided in this embodiment of the invention.

[0026] Figure 7 This is a schematic diagram illustrating the prediction of pure compressor refrigeration in the energy storage intelligent temperature control method provided in this embodiment of the invention.

[0027] Figure 8 This is a schematic diagram of the operation mode of the hidden layer of the intelligent temperature control method for energy storage provided in an embodiment of the present invention.

[0028] Figure 9 This is a schematic diagram illustrating the specific execution logic of the intelligent temperature control method for energy storage provided in an embodiment of the present invention.

[0029] Figure 10 The intelligent temperature control method for energy storage provided in the embodiments of the present invention Figure 11 The intelligent temperature control method for energy storage provided in the embodiments of the present invention Detailed Implementation To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0032] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0033] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0034] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0035] Example Reference Figures 1-11 This is the first embodiment of the present invention, which provides an intelligent temperature control method for energy storage, including: S1: Construct a temperature control model based on deep learning and formulate dynamic control strategies.

[0036] Among them, building a temperature control and management model based on deep learning includes building a deep learning algorithm for intelligent control of temperature control equipment that considers multiple factors based on deep learning technology; Developing dynamic management strategies includes developing dynamic management strategies that respond to changes in the internal and external environment of the energy storage system to meet the temperature requirements and low energy consumption of the cells with high adaptability.

[0037] S2: Set up a deep learning mechanism, select control actions, update the temperature control model based on feedback results, and complete the dynamic optimization of the dynamic control strategy.

[0038] The deep learning mechanism includes states, actions, and rewards. Based on the current environment and operating status Choose the relative action If the result of the action leads to the desired effect or outcome, it will be considered a reward. Otherwise, if the result of the action deviates from the target effect, it will be punished.

[0039] S2.1: Updating the temperature control model based on feedback results includes using a weighted macro average as an evaluation index for the prediction effect of the classification algorithm in the prediction stage of the temperature control model, which comprehensively reflects the two indicators of reward and punishment. The classification algorithm includes calculating the influence levels of the hidden layers. For each category i at each level, the binary classification result is calculated using the binary classification formula, denoted as . The calculation is performed based on the weight of each level in the hidden layer, and the precision of the reward and the recall of the penalty are calculated.

[0040] S2.2: The formula for allocating weights based on the weights of each level in the hidden layer is as follows:

[0041] in, This represents the total number of levels in the hidden layers. This represents the percentage of influencing parameters in the entire parameter library for the corresponding mode / scenario. For overall weighted macro average value, For the i-th hidden layer category in the binary classification case value, The penalty recall rate is the value corresponding to the i-th hidden layer category. Let be the reward precision corresponding to the i-th hidden layer category.

[0042] Furthermore, the formula for calculating reward accuracy is as follows:

[0043] The formula for calculating penalized recall is:

[0044] in, To predict the number of items that conform to the trend, To predict the number of items that contradict the trend; This represents the total number of cumulative predictions. To reward accuracy, To penalize low recall rates.

[0045] Furthermore, developing a low-energy-consumption temperature control strategy for the next moment includes: Collect operating parameters of the energy storage system, including external dry bulb temperature, internal temperature, internal humidity, charge / discharge power, and cycle count. The collected parameters are input into the deep learning algorithm for predicting the comfort of the battery cells in the cabin, predicting the target temperature control requirement of the battery cells at the next moment, and determining whether the conditions for starting the energy-saving mode are met. If the conditions for starting the energy-saving mode are met, the liquid chiller unit will start the self-circulation and dehumidification air conditioning combination mode. The temperature control speed of the liquid chiller pump, the opening frequency of the dehumidification air conditioner and the set temperature will be dynamically adjusted according to the algorithm output. The control parameters for starting the energy-saving mode will be sent to the control energy storage system and executed. If the conditions for energy-saving mode are not met, the liquid-cooled unit will start the cooling mode. Depending on the intensity of cooling demand, the dry cooler will be selected for natural heat dissipation, coupled cooling of the dry cooler and compressor, or pure compressor cooling. The outlet water temperature and unit power will be adjusted. After the temperature control of the energy storage system reaches the set target, the corresponding control parameters will be output to the control energy storage system and executed.

[0046] Furthermore, this invention provides an intelligent temperature control method for energy storage. First, a deep learning-based temperature control model is constructed, using multiple factors such as the temperature and humidity inside and outside the energy storage system, charging and discharging power, and cycle count as inputs. This establishes an intelligent control strategy that dynamically responds to environmental changes while balancing cell temperature control requirements and low energy consumption goals. Then, a deep reinforcement learning mechanism is introduced. Through a closed-loop approach of state perception, action execution, and reward feedback, the control effect is evaluated, and model parameters are continuously optimized to improve the accuracy and adaptability of strategy decisions. During execution, real-time operating data is collected and input into a prediction algorithm to determine whether energy-saving conditions are met. If met, the liquid chiller self-circulation and dehumidification air conditioning combination mode is activated, and relevant equipment operating parameters are dynamically adjusted. If not met, different cooling modes such as a dry cooler or compressor are selected according to cooling needs, adjusting the outlet water temperature and unit power. After temperature control stabilizes, control commands are output, achieving intelligent, dynamic, and low-energy-consumption temperature control management throughout the entire process.

[0047] In a preferred embodiment, the energy storage intelligent temperature control system includes a dynamic management strategy module, which constructs a temperature control model based on deep learning and formulates a dynamic management strategy; a dynamic optimization module for the dynamic management strategy, which sets up a deep learning mechanism, selects control actions, updates the temperature control model based on feedback results, and completes the dynamic optimization of the dynamic management strategy; and a temperature control equipment control strategy module, which, based on the updated temperature control model with feedback results, decides on the feedback of the operating status of the energy storage intelligent temperature control system and formulates a low-energy-consumption temperature control equipment control strategy for the next moment.

[0048] The above-mentioned unit modules can be embedded in the processor of the computer device in hardware form or independent of it, or they can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0049] In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be an LCD screen or an e-ink display screen. The input device of the computer device may be a touch layer covering the display screen, or buttons, a trackball, or a touchpad located on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0050] In summary, this invention constructs an intelligent temperature control model based on deep learning and deep reinforcement learning, achieving dynamic optimization and adaptive adjustment of the temperature control strategy for energy storage systems. Compared to the traditional static control method that only fixes the outlet water temperature of the liquid chiller, this invention comprehensively considers multi-dimensional environmental and operational factors such as internal and external temperature, humidity, charging and discharging power, and cycle count. It can sense changes in operating conditions in real time, predict cell temperature control requirements, and dynamically decide on the optimal operating mode and control parameters. By introducing a state-action-reward mechanism for model iterative optimization, combined with weighted macro averaging... The evaluation strategy of the index enables the system to meet the high temperature requirements of the battery cells while prioritizing the use of low-energy-consumption modes such as self-circulation, dehumidification, and dry cooler, which significantly reduces the overall energy consumption and operating noise. This invention improves the adaptability of the temperature control system to complex and variable operating conditions, and achieves a unity of energy saving, accuracy and intelligence. It effectively solves the technical problems of single control strategy, high energy consumption and poor environmental adaptability in the existing technology.

[0051] After introducing the method and system of exemplary embodiments of the present invention, the following references are made. Figure 10 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... Figure 10The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above method implementation, such as: constructing a temperature control model based on deep learning and formulating a dynamic control strategy; setting up a deep learning mechanism, selecting control actions, updating the temperature control model based on feedback results, and completing the dynamic optimization of the dynamic control strategy; and, based on the updated temperature control model, deciding on the feedback of the operating status of the energy storage intelligent temperature control system and formulating a low-energy-consumption temperature control equipment control strategy for the next moment. The specific implementation methods of each step will not be repeated here.

[0052] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0053] After introducing the methods and media of exemplary embodiments of the present invention, the following references are made. Figure 11 A computational device for adaptive recovery of low-voltage power grid self-healing control according to an exemplary embodiment of the present invention.

[0054] Figure 11 A block diagram is shown of an exemplary computing device 40 suitable for implementing embodiments of the present invention. The computing device 40 may be a computer system or a server. Figure 11 The computing device 40 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0055] like Figure 11 As shown, the components of computing device 40 may include, but are not limited to: one or more processors or processing units 401, system memory 402, and bus 403 connecting different system components (including system memory 402 and processing unit 401).

[0056] The computing device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 40, including volatile and non-volatile media, and removable and non-removable media.

[0057] System memory 402 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 4021 and / or cache memory 4022. Computing device 40 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 4023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 11 Not shown in the image (usually referred to as a "hard drive"). Although not shown in Figure 11 The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 403 via one or more data media interfaces. System memory 402 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0058] A program / utility 4025 having a set (at least one) of program modules 4024 may be stored, for example, in system memory 402, and such program modules 4024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 4024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0059] The computing device 40 can also communicate with one or more external devices 404 (such as a keyboard, pointing device, display, etc.). This communication can be performed via the input / output (I / O) interface 405. Furthermore, the computing device 40 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 406. Figure 11 As shown, network adapter 406 communicates with other modules of computing device 40 (such as processing unit 401) via bus 403. It should be understood that, although... Figure 11 As not shown, it can be used in conjunction with computing device 40 with other hardware and / or software modules.

[0060] The processing unit 401 executes various functional applications and data processing by running programs stored in the system memory 402. For example, it constructs a temperature control and management model based on deep learning and formulates dynamic control strategies; sets up a deep learning mechanism, selects control actions, updates the temperature control and management model based on feedback results, and completes dynamic optimization of dynamic control strategies; and, based on the updated temperature control and management model, decides on the feedback of the operating status of the energy storage intelligent temperature control system and formulates a low-energy-consumption temperature control equipment control strategy for the next moment.

[0061] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0062] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0063] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0065] If the functionality is implemented as a software functional unit and sold or used as an independent product, it can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0067] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0068] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An intelligent temperature control method for energy storage, characterized in that: include, Construct a temperature control model based on deep learning and formulate dynamic control strategies; Set up a deep learning mechanism, select control actions, update the temperature control model based on feedback results, and complete the dynamic optimization of the dynamic control strategy; Based on the updated temperature control model according to the feedback results, the decision-making system formulates a low-energy-consumption temperature control strategy for the next moment based on the feedback of the operating status of the intelligent temperature control system for energy storage.

2. The intelligent temperature control method for energy storage as described in claim 1, characterized in that: The construction of the temperature control model based on deep learning includes building a deep learning algorithm for intelligent control of temperature control equipment that considers multiple factors based on deep learning technology. The formulation of dynamic management and control strategies includes developing dynamic management and control strategies that respond to changes in the internal and external environment of the energy storage system to meet the temperature requirements and low energy consumption of the battery cells with high adaptability.

3. The intelligent temperature control method for energy storage as described in claim 2, characterized in that: The process of the deep learning mechanism includes states, actions, and rewards; Based on the current environment and operating status Choose the relative action If the result of the action leads to the desired effect or outcome, it will be considered a reward. Otherwise, if the result of the action deviates from the target effect, it will be punished.

4. The intelligent temperature control method for energy storage as described in claim 3, characterized in that: The method of updating the temperature control model based on feedback results includes using a weighted macro average as an evaluation index for the prediction effect of the classification algorithm in the prediction stage of the temperature control model, which comprehensively reflects both reward and punishment indicators. The classification algorithm includes calculating the influence levels for the hidden layers. For each category i at each level, the binary classification result is calculated using the binary classification formula, denoted as... The calculation is performed based on the weight of each level in the hidden layer, and the precision of the reward and the recall of the penalty are calculated.

5. The intelligent temperature control method for energy storage as described in claim 4, characterized in that: The formula for allocating weights based on the weights of each level in the hidden layer is as follows: in, This represents the total number of levels in the hidden layers. This represents the percentage of influencing parameters in the entire parameter library for the corresponding mode / scenario. For overall weighted macro average value, For the i-th hidden layer category in the binary classification case value, The penalty recall rate is the value corresponding to the i-th hidden layer category. Let be the reward precision corresponding to the i-th hidden layer category.

6. The intelligent temperature control method for energy storage as described in claim 5, characterized in that: The formula for calculating the reward accuracy is: The formula for calculating the penalty recall rate is as follows: in, To predict the number of items that conform to the trend, To predict the number of items that contradict the trend; This represents the total number of cumulative predictions. To reward accuracy, To penalize low recall rates.

7. The intelligent temperature control method for energy storage as described in claim 6, characterized in that: The strategy for developing a low-energy-consumption temperature control device for the next moment includes: Collect operating parameters of the energy storage system, including external dry bulb temperature, internal temperature, internal humidity, charge / discharge power, and cycle count. The collected parameters are input into the deep learning algorithm for predicting the comfort of the battery cells in the cabin, predicting the target temperature control of the battery cells at the next moment, and determining whether the conditions for starting the energy-saving mode are met. If the conditions for starting the energy-saving mode are met, the liquid chiller unit will start the self-circulation and dehumidification air conditioning combination mode. The temperature control speed of the liquid chiller pump, the opening frequency of the dehumidification air conditioner and the set temperature will be dynamically adjusted according to the algorithm output. The control parameters for starting the energy-saving mode will be sent to the control energy storage system and executed. If the conditions for energy-saving mode are not met, the liquid-cooled unit will start the cooling mode. Depending on the intensity of cooling demand, the dry cooler will be selected for natural heat dissipation, coupled cooling of the dry cooler and compressor, or pure compressor cooling. The outlet water temperature and unit power will be adjusted. After the temperature control of the energy storage system reaches the set target, the corresponding control parameters will be output to the control energy storage system and executed.

8. An intelligent temperature control system for energy storage, based on the intelligent temperature control method for energy storage according to any one of claims 1 to 7, characterized in that: include, The dynamic control strategy module constructs a temperature control model based on deep learning and formulates dynamic control strategies. The dynamic optimization module of the dynamic control strategy sets up a deep learning mechanism, selects control actions, updates the temperature control model based on feedback results, and completes the dynamic optimization of the dynamic control strategy. The temperature control equipment control strategy module updates the temperature control management model based on feedback results, decides on the operating status feedback of the energy storage intelligent temperature control system, and formulates a low-energy-consumption temperature control equipment control strategy for the next moment.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the energy storage intelligent temperature control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the energy storage intelligent temperature control method according to any one of claims 1 to 7.

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